**6. Conclusions**

This paper provides a novel knowledge embedded sample model and deep semi-supervised learning algorithm to detect NTL by using SM data. We first analyzed the characteristic of realistic NTL, and design a knowledge embedded sample model refer to the principle of electricity measurement. Next, we proposed an autoencoder based semi-supervised learning model. To avoid overfitting, we designed a regularization module, loss and training algorithm. Overall, our scheme outperforms all baselines and state-of-the-art results. In future work, it is promising to explore a new sample model and deep neural networks to adapt to possible public datasets.

**Author Contributions:** Conceptualization, X.L. and Y.Z.; methodology, X.L., Y.Z. and Y.Y.; validation, X.L. and L.F.; resources, L.F.; writing—original draft preparation, X.L., Z.W. and Y.Y.; writing—review and editing, F.W.; supervision, Z.W.

**Funding:** This research was funded by State Grid Jiangsu Electric Power Co., Ltd. Science and Technology Project: Research on Key Technologies of Big Customers Abnormal Electricity Consumption Diagnosis Based on Deep Adversarial Learning (grant number: J2019048).

**Conflicts of Interest:** The authors declare no conflict of interest.
